

'''simple docstring'''
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_lowerCamelCase      					=     logging.get_logger(__name__)

_lowerCamelCase      					=     {
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}
class 					_snake_case		(__SCREAMING_SNAKE_CASE):
			__A  :	List[Any]        ="t5"
			__A  :	List[Any]        =["past_key_values"]
			__A  :	int        ={"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}






			def __init__(  self			,_snake_case=3_21_28			,_snake_case=5_12			,_snake_case=64			,_snake_case=20_48			,_snake_case=6			,_snake_case=None			,_snake_case=8			,_snake_case=32			,_snake_case=1_28			,_snake_case=0.1			,_snake_case=1E-6			,_snake_case=1.0			,_snake_case="relu"			,_snake_case=True			,_snake_case=True			,_snake_case=0			,_snake_case=1			,**_snake_case			,):
				UpperCAmelCase_     :      int						=  vocab_size
				UpperCAmelCase_     :      str						=  d_model
				UpperCAmelCase_     :      Any						=  d_kv
				UpperCAmelCase_     :      Any						=  d_ff
				UpperCAmelCase_     :      str						=  num_layers
				UpperCAmelCase_     :      List[Any]						=  (
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				UpperCAmelCase_     :      Optional[int]						=  num_heads
				UpperCAmelCase_     :      List[str]						=  relative_attention_num_buckets
				UpperCAmelCase_     :      int						=  relative_attention_max_distance
				UpperCAmelCase_     :      Union[str, Any]						=  dropout_rate
				UpperCAmelCase_     :      Optional[Any]						=  layer_norm_epsilon
				UpperCAmelCase_     :      Any						=  initializer_factor
				UpperCAmelCase_     :      List[Any]						=  feed_forward_proj
				UpperCAmelCase_     :      List[str]						=  use_cache

				UpperCAmelCase_     :      Any						=  self.feed_forward_proj.split("-"     )
				UpperCAmelCase_     :      Union[str, Any]						=  act_info[-1]
				UpperCAmelCase_     :      str						=  act_info[0] == "gated"

				if len(_snake_case     ) > 1 and act_info[0] != "gated" or len(_snake_case     ) > 2:
					raise ValueError(
					    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
					    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
					    "'gated-gelu' or 'relu'"     )

				# for backwards compatibility
				if feed_forward_proj == "gated-gelu":
					UpperCAmelCase_     :      Any						=  "gelu_new"

				super().__init__(
				    pad_token_id=_snake_case			,eos_token_id=_snake_case			,is_encoder_decoder=_snake_case			,**_snake_case			,)




class 					_snake_case		(__SCREAMING_SNAKE_CASE):






			@property
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Tuple						=  {
				    "input_ids": {0: "batch", 1: "encoder_sequence"},
				    "attention_mask": {0: "batch", 1: "encoder_sequence"},
				}
				if self.use_past:
					UpperCAmelCase_     :      Union[str, Any]						=  "past_encoder_sequence + sequence"
					UpperCAmelCase_     :      Any						=  {0: "batch"}
					UpperCAmelCase_     :      Dict						=  {0: "batch", 1: "past_decoder_sequence + sequence"}
				else:
					UpperCAmelCase_     :      List[Any]						=  {0: "batch", 1: "decoder_sequence"}
					UpperCAmelCase_     :      List[Any]						=  {0: "batch", 1: "decoder_sequence"}

				if self.use_past:
					self.fill_with_past_key_values_(_snake_case			,direction="inputs"     )

				return common_inputs






			@property
			def 		UpperCamelCase__							(  self     ):
				return 13





def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'							)

		_lowerCamelCase				   =  str(bin(UpperCamelCase							)							)
		binary_number += "0" * shift_amount
		return binary_number


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'							)

		_lowerCamelCase				   =  str(bin(UpperCamelCase							)							)[2:]
		if shift_amount >= len(UpperCamelCase							):
				return "0b0"
		_lowerCamelCase				   =  binary_number[: len(UpperCamelCase							) - shift_amount]
		return "0b" + shifted_binary_number


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number >= 0:  # Get binary representation of positive number
				_lowerCamelCase				   =  '0' + str(bin(UpperCamelCase							)							).strip('-'							)[2:]
		else:  # Get binary (2's complement) representation of negative number
				_lowerCamelCase				   =  len(bin(UpperCamelCase							)[3:]							)  # Find 2's complement of number
				_lowerCamelCase				   =  bin(abs(UpperCamelCase							) - (1 << binary_number_length)							)[3:]
				_lowerCamelCase				   =  (
				    '1' + '0' * (binary_number_length - len(UpperCamelCase							)) + binary_number
				)

		if shift_amount >= len(UpperCamelCase							):
				return "0b" + binary_number[0] * len(UpperCamelCase							)
		return (
		    "0b"
		    + binary_number[0] * shift_amount
		    + binary_number[: len(UpperCamelCase							) - shift_amount]
		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()